Relationship between oral microbiota and colorectal cancer: A systematic review
Bibliographic record
Abstract
This systematic review aims to investigate the microbial basis underlying the association between oral microbiota and colorectal cancer. A comprehensive search was conducted across four databases, encompassing potentially relevant studies published up to April 2024 related to the PECO question: "Is there a differentiation in oral microbial composition between adult patients diagnosed with colorectal cancer compared to healthy patients?". The Newcastle-Ottawa Scale was used to evaluate the quality of the studies included. The level of evidence was assessed through the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) tool. Sixteen studies fulfilled the eligibility criteria. Based on low to moderate evidence profile, high levels of certain subspecies within Firmicutes (such as Streptococcus anginosus, Peptostreptococcus stomatis, S. koreensis, and S. gallolyticus), Prevotella intermedia, Fusobacterium nucleatum, and Neisseria oralis were found to be associated with colorectal cancer. Conversely, certain bacteria (e.g., Lachnospiraceae, F. periodonticum, and P. melaninogenica) could exert a symbiotic protective effect against colorectal cancer. Based on existing evidence, it appears that variations in oral microbiota composition exist among individuals with and without colorectal cancer. However, further research is necessary to determine the mechanisms of oral dysbiosis in colorectal carcinogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".